ERP: Edge Resource Pooling for Data Stream Mobile Computing

Liu, Junkai; Luo, Ke; Zhou, Zhi; Xu, Chen

doi:10.1109/jiot.2018.2882588

Cited by 27 publications

(5 citation statements)

References 42 publications

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“…In this case, the capacity of the metaverse network will increase without occupying the resources of the cloud server [80]. The authors in [81] have presented an efficient method for sharing resources at the edge of the network in the framework of the resource pool. For a survey of incentive mechanisms for sharing resources at the network edge, see [82].…”

Section: A Metaverse Requirements To Be Met By Edge Computingmentioning

confidence: 99%

Unlocking Metaverse-as-a-Service The three pillars to watch: Privacy and Security, Edge Computing, and Blockchain

Ahsani¹,

Rahimi²,

Letafati³

et al. 2023

Preprint

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In this article, the authors provide a comprehensive overview on three core pillars of metaverse-as-a-service (MaaS) platforms; privacy and security, edge computing, and blockchain technology. The article starts by investigating security aspects for the wireless access to the metaverse. Then it goes through the privacy and security issues inside the metaverse from data-centric, learning-centric, and human-centric points-of-view. The authors address private and secure mechanisms for privatizing sensitive data attributes and securing machine learning algorithms running in a distributed manner within the metaverse platforms. Novel visions and less-investigated methods are reviewed to help mobile network operators and metaverse service providers facilitate the realization of secure and private MaaS through different layers of the metaverse, ranging from the access layer to the social interactions among clients. Later in the article, it has been explained how the paradigm of edge computing can strengthen different aspects of the metaverse. Along with that, the challenges of using edge computing in the metaverse have been comprehensively investigated. Additionally, the paper has comprehensively investigated and analyzed 10 main challenges of MaaS platforms and thoroughly discussed how blockchain technology provides solutions for these constraints. At the final, future vision and directions, such as content-centric security and zero-trust metaverse, some blockchain's unsolved challenges are also discussed to bring further insights for the network designers in the metaverse era.

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Section: A Metaverse Requirements To Be Met By Edge Computingmentioning

confidence: 99%

Unlocking Metaverse-as-a-Service The three pillars to watch: Privacy and Security, Edge Computing, and Blockchain

Ahsani¹,

Rahimi²,

Letafati³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…For tractable analysis, we further assume uniform distributions over [0, 1], and denote by g wc (w, c) the joint distribution over (w, c) ∈ [0, 1] × [0, 1]. 4 As we have mentioned, for a particular content, an ED can choose to act as an agent or a requester for obtaining the content, or to act as an alien who does not participate. To specify EDs' choices, we denote by z wc ∈ {AG, RE, NA} the role selection strategy of a type-(w, c) ED, where • z wc = AG: choosing to be an agent for the content; • z wc = RE: choosing to be a requester for the content; • z wc = NA: choosing to be an alien for the content.…”

Section: A Strategy Profile Of Edsmentioning

confidence: 99%

Crowd-MECS: A Novel Crowdsourcing Framework for Mobile Edge Caching and Sharing

Jiang

Gao

Wang

et al. 2020

IEEE Internet Things J.

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Crowdsourced mobile edge caching and sharing (Crowd-MECS) is emerging as a promising content delivery paradigm by employing a large crowd of existing edge devices (EDs) to cache and share popular contents. The successful technology adoption of Crowd-MECS relies on a comprehensive understanding of the complicated economic interactions and strategic decision-making of different stakeholders. In this paper, we focus on studying the economic and strategic interactions between one content provider (CP) and a large crowd of EDs, where the EDs can decide whether to cache and share contents for the CP, and the CP can decide to share a certain revenue with EDs as the incentive of caching and sharing contents. We formulate such an interaction as a two-stage Stackelberg game. In Stage I, the CP aims to maximize its own profit by deciding the ratio of revenue shared with EDs. In Stage II, EDs aim to maximize their own payoffs by choosing to be agents who cache and share contents, and meanwhile gain a certain revenue from the CP, or requesters who do not cache but request contents in the on-demand fashion. We first analyze the EDs' best responses and prove the existence and uniqueness of the equilibrium in Stage II by using the non-atomic game theory. Then, we identify the piece-wise structure and the unimodal feature of the CP's profit function, based on which we design a tailored low-complexity one-dimensional search algorithm to achieve the optimal revenue sharing ratio for the CP in Stage I. Simulation results show that both the CP's profit and the EDs' total welfare can be improved significantly (e.g., by 120% and 50%, respectively) by using the proposed Crowd-MECS, comparing with the Non-MEC system where the CP serves all EDs directly.

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“…Due to the broader usage of IoT devices, endpoints have to meet different bandwidths and latency requirements, depending on their applications [ 1 ]. At the same time, the operating environment for these devices has added new challenges to the design, including more constraints in power consumption, remote accessibility, limited memory, or processing capability [ 2 , 3 ]. These demands can lead to data loss in transmission due to network congestion and dropped packets.…”

Section: Introductionmentioning

confidence: 99%

Cost-Effective Network Reordering Using FPGA

Hoang

Chen

2023

Sensors

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The advancement of complex Internet of Things (IoT) devices in recent years has deepened their dependency on network connectivity, demanding low latency and high throughput. At the same time, expanding operating conditions for these devices have brought challenges that limit the design constraints and accessibility for future hardware or software upgrades. These limitations can result in data loss because of out-of-order packets if the design specification cannot keep up with network demands. In addition, existing network reordering solutions become less applicable due to the drastic changes in the type of network endpoints, as IoT devices typically have less memory and are likely to be power-constrained. One approach to address this problem is reordering packets using reconfigurable hardware to ease computation in other functions. Field Programmable Gate Array (FPGA) devices are ideal candidates for hardware implementations at the network endpoints due to their high performance and flexibility. Moreover, previous research on packet reordering using FPGAs has serious design flaws that can lead to unnecessary packet dropping due to blocking in memory. This research proposes a scalable hardware-focused method for packet reordering that can overcome the flaws from previous work while maintaining minimal resource usage and low time complexity. The design utilizes a pipelined approach to perform sorting in parallel and completes the operation within two clock cycles. FPGA resources are optimized using a two-layer memory management system that consumes minimal on-chip memory and registers. Furthermore, the design is scalable to support multi-flow applications with shared memories in a single FPGA chip.

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ERP: Edge Resource Pooling for Data Stream Mobile Computing

Cited by 27 publications

References 42 publications

Unlocking Metaverse-as-a-Service The three pillars to watch: Privacy and Security, Edge Computing, and Blockchain

Unlocking Metaverse-as-a-Service The three pillars to watch: Privacy and Security, Edge Computing, and Blockchain

Crowd-MECS: A Novel Crowdsourcing Framework for Mobile Edge Caching and Sharing

Cost-Effective Network Reordering Using FPGA

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